Classification of batteries in waste streams using magnetic induction spectroscopy

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Magnetic induction is widely used to detect and classify metal objects over a range of applications; this paper considers the potential of this technique to inspect for the presence and characteristics of batteries within waste streams. As the number of batteries used across the world increases, an efficient method is needed to ensure batteries can be classified to allow for more efficient recycling. The detection of batteries would also reduce the risk of fire and pollution by identifying the battery before they are crushed or shredded.In this study, a magnetic induction sensor measured the batteries and scrap metal between 781 Hz and 95282 Hz to allow a significant frequency range to be observed. The real component (in-phase) of a battery’s electromagnetic response is different from scrap metal; this could allow for an algorithm to be trained to detect batteries within metal waste or when inside non-metallic objects. The response observed shows that batteries could be grouped into size, which is useful if no line of sight is available, which a traditional camera system requires. Once grouped into size the batteries could be further separated according to their internal contents when the real component is used; this would reduce the risk of cross-contamination when they are recycled. The real component response of lithium and NiMH batteries is different when compared to the other batteries; this could allow them to be detected and removed from a waste stream, which is important as lithium batteries can set on fire.
Original languageEnglish
Title of host publication2022 IEEE Sensors Applications Symposium, SAS 2022 - Proceedings
Number of pages6
Publication statusPublished - 12 Sept 2022


  • Spectroscopy
  • Magnetic separation
  • Metals
  • Batteries
  • Recycling
  • Frequency measurement
  • Cameras
  • Electromagnetic induction
  • Waste recovery
  • Magnetic induction spectra


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  • Electromagnetic Sensing Group

    Peyton, A., Fletcher, A., Daniels, D., Conniffe, D., Cheadle, E., Podd, F., Xu, H., Tesfalem, H., Davidson, J., Anderson, J., Tang, J., Hampton, J., Wilson, J., Barratt, K., Marsh, L., Chen, L., O'Toole, M., Lu, M., Ran, Q., Huang, R., Watson, S., Ozdeger, T., Van Verre, W., Yin, W., Gao, X., Tao, Y., Chen, Z., Regan, A., Li, J. & Cheng, Q.

    1/10/04 → …

    Project: Research

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